Fine-grained image retrieval by combining attention mechanism and context information

Recently, fine-grained image retrieval (FGIR) has become a hot topic in computer vision. Most of the advanced retrieval algorithms in this field mainly focus on the construction of loss function and the design of hard sample mining strategy. In this paper, we improve the performance of the FGIR algorithm from another perspective and propose an attention mechanism and context Information constraints-based image retrieval (AMCICIR) method for FGIR. It first applies an attention learning mechanism to gradually refine object location and extracts useful local features from coarse to fine. Then, it uses an improved graph convolutional network (GCN), where the adjacency matrix is dynamically adjusted with the current features and model retrieval performances during the model learning, to model the internal semantic interactions of the learned local features, so as to obtain a more discriminative and fine-grained image representation. Finally, various experiments are conducted on two fine-grained image datasets, CUB-200-2011 and Cars-196, and the experimental results show that the AMCICIR algorithm can outperform pervious state-of-the-art works remarkably.